{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Multi-output Decision Tree Regression\n\n\nAn example to illustrate multi-output regression with decision tree.\n\nThe `decision trees <tree>`\nis used to predict simultaneously the noisy x and y observations of a circle\ngiven a single underlying feature. As a result, it learns local linear\nregressions approximating the circle.\n\nWe can see that if the maximum depth of the tree (controlled by the\n`max_depth` parameter) is set too high, the decision trees learn too fine\ndetails of the training data and learn from the noise, i.e. they overfit.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Create a random dataset\nrng = np.random.RandomState(1)\nX = np.sort(200 * rng.rand(100, 1) - 100, axis=0)\ny = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T\ny[::5, :] += (0.5 - rng.rand(20, 2))\n\n# Fit regression model\nregr_1 = DecisionTreeRegressor(max_depth=2)\nregr_2 = DecisionTreeRegressor(max_depth=5)\nregr_3 = DecisionTreeRegressor(max_depth=8)\nregr_1.fit(X, y)\nregr_2.fit(X, y)\nregr_3.fit(X, y)\n\n# Predict\nX_test = np.arange(-100.0, 100.0, 0.01)[:, np.newaxis]\ny_1 = regr_1.predict(X_test)\ny_2 = regr_2.predict(X_test)\ny_3 = regr_3.predict(X_test)\n\n# Plot the results\nplt.figure()\ns = 25\nplt.scatter(y[:, 0], y[:, 1], c=\"navy\", s=s,\n            edgecolor=\"black\", label=\"data\")\nplt.scatter(y_1[:, 0], y_1[:, 1], c=\"cornflowerblue\", s=s,\n            edgecolor=\"black\", label=\"max_depth=2\")\nplt.scatter(y_2[:, 0], y_2[:, 1], c=\"red\", s=s,\n            edgecolor=\"black\", label=\"max_depth=5\")\nplt.scatter(y_3[:, 0], y_3[:, 1], c=\"orange\", s=s,\n            edgecolor=\"black\", label=\"max_depth=8\")\nplt.xlim([-6, 6])\nplt.ylim([-6, 6])\nplt.xlabel(\"target 1\")\nplt.ylabel(\"target 2\")\nplt.title(\"Multi-output Decision Tree Regression\")\nplt.legend(loc=\"best\")\nplt.show()"
      ]
    }
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      "file_extension": ".py",
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